<![CDATA[In carrying out an activity regularly and smoothly, it is necessary to make an activity schedule that can manage the time of one activity with another so that unwanted things do not happen such as the same time, the same place, and others. Making a schedule of activities is quite easy to do if there are not too many entities involved and if the entities are not tied to each other, but for larger cases, creating a schedule of activities manually will take quite a lot of time and can result in errors in the schedule or shortages. effectiveness in the resulting schedule. This is commonly experienced in making course schedules at universities because there are a lot of course data and lecturers can teach several courses at once and at different times, therefore in making course schedules can be done by applying genetic algorithms so that the time required needed in making the course schedule shorter and the results obtained can be more optimal than the results of making the course schedule manually. In this study, the optimal course schedule was obtained in the 31st generation using data on rooms, courses, study time, lecturers, and departments so that one chromosome has 154 gen, then the population length is made up to 9 individuals or chromosomes, the mutation rate is set at 0.1, and the method used in the individual selection stage is the tournament selection method where the tournament size is set at 3. The fitness value is taken so that a schedule is said to be optimal, i.e. if the fitness value is equal to 1 because then it shows that there are no errors or problems (such as time, lecturers, conflicting rooms) that occur in the schedule.]]>
